Optimizing Hyperparameters for Machine Learning Algorithms in Production

Machine learning offers the potential to train data-based models and therefore to extract knowledge from data. One key design decision is the selection of hyperparameters of a machine learning algorithm. Hyperparameter optimization techniques automatically tune the hyperparameters ¿ but choosing the best technique depends highly on the use case. This book presents a decision support system for the selection of hyperparameter optimization techniques in the production domain.